人工智能可以依靠真理的系统性吗? 模拟规范领域的挑战
1Institute of Philosophy, University of Bern, Laenggassstrasse 49a, 3012 Bern, Switzerland.
概括
大型语言模型 (LLM) 假设真理是系统的,用于世界建模. 然而,规范领域是非系统的,挑战了LLM的进步,并在实际审议中需要人类代理.
科学领域:
- 人工智能的哲学 人工智能的哲学
- 认识论的认识论学.
- 认知科学 认知科学
背景情况:
- 乐观主义围绕着世界建模的大型语言模型 (LLM),基于假设真理是系统的 (一致和连贯的).
- 这种系统性理论上允许LLM推断真相并纠正数据的不准确性,提高全面性.
- 哲学论证表明,在规范领域的真理可能在很大程度上是非系统的.
研究的目的:
- 调查真相系统性对大语言模型 (LLM) 开发的影响.
- 检查LLM能力在规范领域的非系统性所带来的挑战.
- 根据这些挑战,重新评估依赖LLM进行实际审议的可能性.
主要方法:
- 哲学论证的论证方式
- 真理和系统性的概念分析.
- 对LLM能力和局限性的分析.
主要成果:
- 系统真理的假设对于全面世界建模的LLM进步至关重要.
- 在规范领域的非系统性对LLM来说是一个重大障碍,因为它们无法利用推断的相互联系.
- 法律法学在准确建模规范真理方面的能力受到这些领域内在性质的限制.
结论:
- 由于规范真理是非系统的,LLM在实现全面和准确的表示方面面临更大的困难.
- 在建模系统性规范真理方面,LLM的局限性需要继续依赖人类机构进行实际审议.
- 在规范领域减少对LLM系统性的依赖意味着人类判断和道德推理的作用更大.
关键词:
人工智能伦理学代理机构 代理机构 代理机构人工智能的人工智能是人工智能.真实性 真实性 真实性一致性 一致性价值的冲突 价值的冲突一致性 一致性 一致性艰难的选择 艰难的选择语言模型 语言模型规范性 规范性是指规范性.价值多元主义是一种价值多元主义.更多相关视频
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